In [4]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
from sklearn.linear_model import LinearRegression
In [18]:
Emp=pd.read_csv('Employee_perfromance_data.csv')
In [19]:
Emp.head()
Out[19]:
Employee_ID Department Gender Age Job_Title Hire_Date Years_At_Company Education_Level Performance_Score Monthly_Salary Work_Hours_Per_Week Projects_Handled Overtime_Hours Sick_Days Remote_Work_Frequency Team_Size Training_Hours Promotions Employee_Satisfaction_Score Resigned
0 1 IT Male 55 Specialist 03:05.6 2 High School 5 6750 33 32 22 2 0 14 66 0 2.63 False
1 2 Finance Male 29 Developer 03:05.6 0 High School 5 7500 34 34 13 14 100 12 61 2 1.72 False
2 3 Finance Male 55 Specialist 03:05.6 8 High School 3 5850 37 27 6 3 50 10 1 0 3.17 False
3 4 Customer Support Female 48 Analyst 03:05.6 7 Bachelor 2 4800 52 10 28 12 100 10 0 1 1.86 False
4 5 Engineering Female 36 Analyst 03:05.6 3 Bachelor 2 4800 38 11 29 13 100 15 9 1 1.25 False
In [9]:
Emp.shape
Out[9]:
(100000, 20)
In [10]:
Emp.describe()
Out[10]:
Employee_ID Age Years_At_Company Performance_Score Monthly_Salary Work_Hours_Per_Week Projects_Handled Overtime_Hours Sick_Days Remote_Work_Frequency Team_Size Training_Hours Promotions Employee_Satisfaction_Score
count 100000.000000 100000.000000 100000.000000 100000.000000 100000.000000 100000.000000 100000.000000 100000.000000 100000.000000 100000.000000 100000.000000 100000.000000 100000.000000 100000.000000
mean 50000.500000 41.029410 4.476070 2.995430 6403.211000 44.956950 24.431170 14.514930 7.008550 50.090500 10.013560 49.506060 0.999720 2.999088
std 28867.657797 11.244121 2.869336 1.414726 1372.508717 8.942003 14.469584 8.664026 4.331591 35.351157 5.495405 28.890383 0.815872 1.150719
min 1.000000 22.000000 0.000000 1.000000 3850.000000 30.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000
25% 25000.750000 31.000000 2.000000 2.000000 5250.000000 37.000000 12.000000 7.000000 3.000000 25.000000 5.000000 25.000000 0.000000 2.010000
50% 50000.500000 41.000000 4.000000 3.000000 6500.000000 45.000000 24.000000 15.000000 7.000000 50.000000 10.000000 49.000000 1.000000 3.000000
75% 75000.250000 51.000000 7.000000 4.000000 7500.000000 53.000000 37.000000 22.000000 11.000000 75.000000 15.000000 75.000000 2.000000 3.990000
max 100000.000000 60.000000 10.000000 5.000000 9000.000000 60.000000 49.000000 29.000000 14.000000 100.000000 19.000000 99.000000 2.000000 5.000000
In [11]:
Emp.info
Out[11]:
<bound method DataFrame.info of        Employee_ID        Department  Gender  Age   Job_Title Hire_Date  \
0                1                IT    Male   55  Specialist   03:05.6   
1                2           Finance    Male   29   Developer   03:05.6   
2                3           Finance    Male   55  Specialist   03:05.6   
3                4  Customer Support  Female   48     Analyst   03:05.6   
4                5       Engineering  Female   36     Analyst   03:05.6   
...            ...               ...     ...  ...         ...       ...   
99995        99996           Finance    Male   27  Technician   03:05.6   
99996        99997                IT  Female   36  Consultant   03:05.6   
99997        99998        Operations    Male   53     Analyst   03:05.6   
99998        99999                HR  Female   22  Consultant   03:05.6   
99999       100000           Finance  Female   43     Analyst   03:05.6   

       Years_At_Company Education_Level  Performance_Score  Monthly_Salary  \
0                     2     High School                  5            6750   
1                     0     High School                  5            7500   
2                     8     High School                  3            5850   
3                     7        Bachelor                  2            4800   
4                     3        Bachelor                  2            4800   
...                 ...             ...                ...             ...   
99995                 1        Bachelor                  4            4900   
99996                 6          Master                  5            8250   
99997                 8     High School                  2            4800   
99998                 9     High School                  5            8250   
99999                 0             PhD                  1            4400   

       Work_Hours_Per_Week  Projects_Handled  Overtime_Hours  Sick_Days  \
0                       33                32              22          2   
1                       34                34              13         14   
2                       37                27               6          3   
3                       52                10              28         12   
4                       38                11              29         13   
...                    ...               ...             ...        ...   
99995                   55                46               5          3   
99996                   39                35               7          0   
99997                   31                13               6          5   
99998                   35                43              10          1   
99999                   51                43              27         11   

       Remote_Work_Frequency  Team_Size  Training_Hours  Promotions  \
0                          0         14              66           0   
1                        100         12              61           2   
2                         50         10               1           0   
3                        100         10               0           1   
4                        100         15               9           1   
...                      ...        ...             ...         ...   
99995                     75         16              48           2   
99996                      0         10              77           1   
99997                      0          5              87           1   
99998                     75          2              31           1   
99999                     75         13              45           1   

       Employee_Satisfaction_Score  Resigned  
0                             2.63     False  
1                             1.72     False  
2                             3.17     False  
3                             1.86     False  
4                             1.25     False  
...                            ...       ...  
99995                         1.28     False  
99996                         3.48      True  
99997                         2.60     False  
99998                         3.10     False  
99999                         2.64     False  

[100000 rows x 20 columns]>
In [12]:
sns.pairplot(Emp)

C:\Users\admin\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\admin\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\admin\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\admin\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\admin\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\admin\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\admin\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\admin\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\admin\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\admin\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\admin\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\admin\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\admin\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\admin\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\admin\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\admin\anaconda3\Lib\site-packages\seaborn\_stats\counting.py:137: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.
  bin_edges = np.histogram_bin_edges(vals, bins, binrange, weight)
C:\Users\admin\anaconda3\Lib\site-packages\seaborn\_stats\counting.py:176: RuntimeWarning: Converting input from bool to <class 'numpy.uint8'> for compatibility.
  hist, edges = np.histogram(vals, **bin_kws, weights=weights, density=density)
Out[12]:
<seaborn.axisgrid.PairGrid at 0x2a3ca4c82d0>
No description has been provided for this image
In [13]:
Emp.head()
Out[13]:
Employee_ID Department Gender Age Job_Title Hire_Date Years_At_Company Education_Level Performance_Score Monthly_Salary Work_Hours_Per_Week Projects_Handled Overtime_Hours Sick_Days Remote_Work_Frequency Team_Size Training_Hours Promotions Employee_Satisfaction_Score Resigned
0 1 IT Male 55 Specialist 03:05.6 2 High School 5 6750 33 32 22 2 0 14 66 0 2.63 False
1 2 Finance Male 29 Developer 03:05.6 0 High School 5 7500 34 34 13 14 100 12 61 2 1.72 False
2 3 Finance Male 55 Specialist 03:05.6 8 High School 3 5850 37 27 6 3 50 10 1 0 3.17 False
3 4 Customer Support Female 48 Analyst 03:05.6 7 Bachelor 2 4800 52 10 28 12 100 10 0 1 1.86 False
4 5 Engineering Female 36 Analyst 03:05.6 3 Bachelor 2 4800 38 11 29 13 100 15 9 1 1.25 False
In [23]:
import seaborn as sns
import matplotlib.pyplot as plt

# Using countplot for categorical data
sns.countplot(x='Gender', data=Emp)

# Show the plot
plt.show()
No description has been provided for this image
In [24]:
# Count the number of males and females in the 'Gender' column
gender_counts = Emp['Gender'].value_counts()

# Display the counts
print(gender_counts)

Gender
Male      48031
Female    48001
Other      3968
Name: count, dtype: int64
In [26]:
Emp.head()
Out[26]:
Employee_ID Department Gender Age Job_Title Hire_Date Years_At_Company Education_Level Performance_Score Monthly_Salary Work_Hours_Per_Week Projects_Handled Overtime_Hours Sick_Days Remote_Work_Frequency Team_Size Training_Hours Promotions Employee_Satisfaction_Score Resigned
0 1 IT Male 55 Specialist 03:05.6 2 High School 5 6750 33 32 22 2 0 14 66 0 2.63 False
1 2 Finance Male 29 Developer 03:05.6 0 High School 5 7500 34 34 13 14 100 12 61 2 1.72 False
2 3 Finance Male 55 Specialist 03:05.6 8 High School 3 5850 37 27 6 3 50 10 1 0 3.17 False
3 4 Customer Support Female 48 Analyst 03:05.6 7 Bachelor 2 4800 52 10 28 12 100 10 0 1 1.86 False
4 5 Engineering Female 36 Analyst 03:05.6 3 Bachelor 2 4800 38 11 29 13 100 15 9 1 1.25 False
In [29]:
# Group the data by 'Department' and count the number of employees in each department
Department = Emp.groupby('Department').size()

# Display the result
print(Department)

Department
Customer Support    11116
Engineering         10956
Finance             11200
HR                  10960
IT                  11131
Legal               11118
Marketing           11216
Operations          11181
Sales               11122
dtype: int64
In [30]:
import matplotlib.pyplot as plt

# Department data (replace with your actual department count data)
department_count = {
    'Customer Support': 11116,
    'Engineering': 10956,
    'Finance': 11200,
    'HR': 10960,
    'IT': 11131,
    'Legal': 11118,
    'Marketing': 11216,
    'Operations': 11181,
    'Sales': 11122
}

# Create a bar plot
plt.figure(figsize=(10,6))
plt.bar(department_count.keys(), department_count.values(), color='skyblue')

# Add labels and title
plt.xlabel('Department')
plt.ylabel('Number of Employees')
plt.title('Number of Employees in Each Department')

# Rotate x-axis labels for better visibility
plt.xticks(rotation=45)

# Show the plot
plt.tight_layout()  # To prevent the labels from being cut off
plt.show()
No description has been provided for this image
In [31]:
pairplot_data = Emp[['Monthly_Salary', 'Work_Hours_Per_Week']]

# Create the pairplot
sns.pairplot(pairplot_data)

# Show the plot
plt.show()

C:\Users\admin\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
C:\Users\admin\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
No description has been provided for this image
In [32]:
Emp.head()
Out[32]:
Employee_ID Department Gender Age Job_Title Hire_Date Years_At_Company Education_Level Performance_Score Monthly_Salary Work_Hours_Per_Week Projects_Handled Overtime_Hours Sick_Days Remote_Work_Frequency Team_Size Training_Hours Promotions Employee_Satisfaction_Score Resigned
0 1 IT Male 55 Specialist 03:05.6 2 High School 5 6750 33 32 22 2 0 14 66 0 2.63 False
1 2 Finance Male 29 Developer 03:05.6 0 High School 5 7500 34 34 13 14 100 12 61 2 1.72 False
2 3 Finance Male 55 Specialist 03:05.6 8 High School 3 5850 37 27 6 3 50 10 1 0 3.17 False
3 4 Customer Support Female 48 Analyst 03:05.6 7 Bachelor 2 4800 52 10 28 12 100 10 0 1 1.86 False
4 5 Engineering Female 36 Analyst 03:05.6 3 Bachelor 2 4800 38 11 29 13 100 15 9 1 1.25 False
In [34]:
sns.displot(Emp['Job_Title'])

C:\Users\admin\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
Out[34]:
<seaborn.axisgrid.FacetGrid at 0x2a3807ff6d0>
No description has been provided for this image
In [35]:
sns.heatmap(Emp.corr(numeric_only=True))
Out[35]:
<Axes: >
No description has been provided for this image
In [36]:
Emp.head()
Out[36]:
Employee_ID Department Gender Age Job_Title Hire_Date Years_At_Company Education_Level Performance_Score Monthly_Salary Work_Hours_Per_Week Projects_Handled Overtime_Hours Sick_Days Remote_Work_Frequency Team_Size Training_Hours Promotions Employee_Satisfaction_Score Resigned
0 1 IT Male 55 Specialist 03:05.6 2 High School 5 6750 33 32 22 2 0 14 66 0 2.63 False
1 2 Finance Male 29 Developer 03:05.6 0 High School 5 7500 34 34 13 14 100 12 61 2 1.72 False
2 3 Finance Male 55 Specialist 03:05.6 8 High School 3 5850 37 27 6 3 50 10 1 0 3.17 False
3 4 Customer Support Female 48 Analyst 03:05.6 7 Bachelor 2 4800 52 10 28 12 100 10 0 1 1.86 False
4 5 Engineering Female 36 Analyst 03:05.6 3 Bachelor 2 4800 38 11 29 13 100 15 9 1 1.25 False
In [37]:
plt.scatter(Emp['Age'],Emp['Gender'])
plt.xlable("Age")
plt.ylable("Gender")

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[37], line 2
      1 plt.scatter(Emp['Age'],Emp['Gender'])
----> 2 plt.xlable("Age")
      3 plt.ylable("Gender")

AttributeError: module 'matplotlib.pyplot' has no attribute 'xlable'
No description has been provided for this image
In [38]:
plt.scatter(Emp['Department'],Emp['Monthly_Salary'])
plt.xlable("Department")
plt.ylable("Monthly_Salary")

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[38], line 2
      1 plt.scatter(Emp['Department'],Emp['Monthly_Salary'])
----> 2 plt.xlable("Department")
      3 plt.ylable("Monthly_Salary")

AttributeError: module 'matplotlib.pyplot' has no attribute 'xlable'
No description has been provided for this image
In [39]:
import seaborn as sns
sns.regplot(x="Years_At_Company",y="Promotions",data=Emp)
Out[39]:
<Axes: xlabel='Years_At_Company', ylabel='Promotions'>
No description has been provided for this image
In [40]:
Emp.head()
Out[40]:
Employee_ID Department Gender Age Job_Title Hire_Date Years_At_Company Education_Level Performance_Score Monthly_Salary Work_Hours_Per_Week Projects_Handled Overtime_Hours Sick_Days Remote_Work_Frequency Team_Size Training_Hours Promotions Employee_Satisfaction_Score Resigned
0 1 IT Male 55 Specialist 03:05.6 2 High School 5 6750 33 32 22 2 0 14 66 0 2.63 False
1 2 Finance Male 29 Developer 03:05.6 0 High School 5 7500 34 34 13 14 100 12 61 2 1.72 False
2 3 Finance Male 55 Specialist 03:05.6 8 High School 3 5850 37 27 6 3 50 10 1 0 3.17 False
3 4 Customer Support Female 48 Analyst 03:05.6 7 Bachelor 2 4800 52 10 28 12 100 10 0 1 1.86 False
4 5 Engineering Female 36 Analyst 03:05.6 3 Bachelor 2 4800 38 11 29 13 100 15 9 1 1.25 False
In [42]:
Emp.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 100000 entries, 0 to 99999
Data columns (total 20 columns):
 #   Column                       Non-Null Count   Dtype  
---  ------                       --------------   -----  
 0   Employee_ID                  100000 non-null  int64  
 1   Department                   100000 non-null  object 
 2   Gender                       100000 non-null  object 
 3   Age                          100000 non-null  int64  
 4   Job_Title                    100000 non-null  object 
 5   Hire_Date                    100000 non-null  object 
 6   Years_At_Company             100000 non-null  int64  
 7   Education_Level              100000 non-null  object 
 8   Performance_Score            100000 non-null  int64  
 9   Monthly_Salary               100000 non-null  int64  
 10  Work_Hours_Per_Week          100000 non-null  int64  
 11  Projects_Handled             100000 non-null  int64  
 12  Overtime_Hours               100000 non-null  int64  
 13  Sick_Days                    100000 non-null  int64  
 14  Remote_Work_Frequency        100000 non-null  int64  
 15  Team_Size                    100000 non-null  int64  
 16  Training_Hours               100000 non-null  int64  
 17  Promotions                   100000 non-null  int64  
 18  Employee_Satisfaction_Score  100000 non-null  float64
 19  Resigned                     100000 non-null  bool   
dtypes: bool(1), float64(1), int64(13), object(5)
memory usage: 14.6+ MB
In [44]:
## Sumarizing the stats of the data

Emp.describe()
Out[44]:
Employee_ID Age Years_At_Company Performance_Score Monthly_Salary Work_Hours_Per_Week Projects_Handled Overtime_Hours Sick_Days Remote_Work_Frequency Team_Size Training_Hours Promotions Employee_Satisfaction_Score
count 100000.000000 100000.000000 100000.000000 100000.000000 100000.000000 100000.000000 100000.000000 100000.000000 100000.000000 100000.000000 100000.000000 100000.000000 100000.000000 100000.000000
mean 50000.500000 41.029410 4.476070 2.995430 6403.211000 44.956950 24.431170 14.514930 7.008550 50.090500 10.013560 49.506060 0.999720 2.999088
std 28867.657797 11.244121 2.869336 1.414726 1372.508717 8.942003 14.469584 8.664026 4.331591 35.351157 5.495405 28.890383 0.815872 1.150719
min 1.000000 22.000000 0.000000 1.000000 3850.000000 30.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 1.000000
25% 25000.750000 31.000000 2.000000 2.000000 5250.000000 37.000000 12.000000 7.000000 3.000000 25.000000 5.000000 25.000000 0.000000 2.010000
50% 50000.500000 41.000000 4.000000 3.000000 6500.000000 45.000000 24.000000 15.000000 7.000000 50.000000 10.000000 49.000000 1.000000 3.000000
75% 75000.250000 51.000000 7.000000 4.000000 7500.000000 53.000000 37.000000 22.000000 11.000000 75.000000 15.000000 75.000000 2.000000 3.990000
max 100000.000000 60.000000 10.000000 5.000000 9000.000000 60.000000 49.000000 29.000000 14.000000 100.000000 19.000000 99.000000 2.000000 5.000000
In [48]:
## Check the missing values.

Emp.isnull()
Out[48]:
Employee_ID Department Gender Age Job_Title Hire_Date Years_At_Company Education_Level Performance_Score Monthly_Salary Work_Hours_Per_Week Projects_Handled Overtime_Hours Sick_Days Remote_Work_Frequency Team_Size Training_Hours Promotions Employee_Satisfaction_Score Resigned
0 False False False False False False False False False False False False False False False False False False False False
1 False False False False False False False False False False False False False False False False False False False False
2 False False False False False False False False False False False False False False False False False False False False
3 False False False False False False False False False False False False False False False False False False False False
4 False False False False False False False False False False False False False False False False False False False False
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
99995 False False False False False False False False False False False False False False False False False False False False
99996 False False False False False False False False False False False False False False False False False False False False
99997 False False False False False False False False False False False False False False False False False False False False
99998 False False False False False False False False False False False False False False False False False False False False
99999 False False False False False False False False False False False False False False False False False False False False

100000 rows × 20 columns

In [47]:
Emp.isnull().sum()
Out[47]:
Employee_ID                    0
Department                     0
Gender                         0
Age                            0
Job_Title                      0
Hire_Date                      0
Years_At_Company               0
Education_Level                0
Performance_Score              0
Monthly_Salary                 0
Work_Hours_Per_Week            0
Projects_Handled               0
Overtime_Hours                 0
Sick_Days                      0
Remote_Work_Frequency          0
Team_Size                      0
Training_Hours                 0
Promotions                     0
Employee_Satisfaction_Score    0
Resigned                       0
dtype: int64
In [ ]:
## Exploratory Data Analysis 
## Correlation
In [ ]:
In [ ]:
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In [ ]:
In [ ]:
In [ ]:
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In [ ]: